Adaptive positioning of drones for enhanced face recognition

ABSTRACT

Presented herein are systems, methods and apparatuses for increasing reliability of face recognition in analysis of images captured by drone mounted imaging sensors, comprising: recognizing a target person in one or more iterations, each iteration comprising: identifying one or more positioning properties of the target person based on analysis of image(s) captured by imaging sensor(s) mounted on a drone operated to approach the target person, instructing the drone to adjust its position to an optimal facial image capturing position selected based on the positioning property(s), receiving facial image(s) of the target person captured by the imaging sensor(s), receiving a face classification associated with a probability score from machine learning model(s) trained to recognize the target person, and initiating another iteration in case the probability score does not exceed a certain threshold. Finally, the face classification may be outputted for use by one or more face recognition based systems.

RELATED APPLICATION(S)

This application claims the benefit of priority under 35 USC § 119(e) ofU.S. Provisional Patent Application No. 62/881,414 filed on Aug. 1,2019, the contents of which are all incorporated by reference as iffully set forth herein in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to facerecognition based on image analysis, and, more specifically, but notexclusively, to enhancing face recognition based on image analysis bydynamically adapting a position of a drone to improve visibility of theface in images captured by drone mounted imaging sensors.

In the past, the use of Unmanned Aerial Vehicles (UAVs) was mainlyrestricted to military applications and uses due to the high cost ofthis technology and the resources required for deploying and maintainingsuch UAVs.

However, recent years have witnessed constant advancements in UAVtechnology, specifically drone technology (increased range, improvedreliability, etc.) and increased availability of cost effective dronesolutions. These have led to appearance and rapid evolution of aplurality of commercial and/or recreational drone based applications,systems and services such as, for example, drone automated deliveryservices, public order systems, security systems, surveillance systemsand/or the like.

Where delivery services are concerned, traditional delivery companiessuch as UPS and FedEx for example, may deliver goods (items) tocustomers using delivery vehicles (e.g., trucks) which are operated bydelivery people. The delivery vehicles travel a predetermined route anddeliver packages to customer locations along the route. At the customerlocation, the delivery person may verify the identity of the person towhom the goods are delivered and/or verify that the delivery location(e.g., customer address) is correct and leave the package in a safeplace at the delivery location.

Many of these drone based applications are based on recognizing andidentifying people. However, since these applications are mostlyautomated, the recognition of the people needs to also be doneautomatically using one or more recognition methods, techniques and/oralgorithms mainly face recognition.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention there is provided acomputer implemented method of increasing reliability of facerecognition in analysis of images captured by drone mounted imagingsensors, comprising:

-   -   Recognizing a target person in one or more iterations. Each        iteration comprising:        -   Identifying one or more positioning properties of the target            person based on analysis of one or more image captured by            one or more imaging sensors mounted on a drone operated to            approach the target person.        -   Instructing the drone to adjust its position to an optimal            facial image capturing position selected based on the one or            more positioning properties.        -   Receiving one or more facial images of the target person            captured by the one or more imaging sensors while the drone            is located at the optimal facial image capturing position.        -   Receiving a face classification associated with a            probability score from one or more machine learning models            trained to recognize the target person which is applied to            the one or more facial images.        -   Initiating another iteration in case the probability score            does not exceed a certain threshold.    -   Outputting the face classification for use by one or more face        recognition based systems.

According to a second aspect of the present invention there is provideda system for increasing reliability of face recognition in analysis ofimages captured by drone mounted imaging sensors, comprising one or moreprocessor executing a code. The code comprising:

-   -   Code instructions to recognize a target person in one or more        iteration. Each iteration comprising:        -   Identifying one or more positioning properties of the target            person based on analysis of one or more image captured by            one or more imaging sensors mounted on a drone operated to            approach the target person.        -   Instructing the drone to adjust its position to an optimal            facial image capturing position selected based on the one or            more positioning properties.        -   Receiving one or more facial images of the target person            captured by the one or more imaging sensors while the drone            is located at the optimal facial image capturing position.        -   Receiving a face classification associated with a            probability score from one or more machine learning models            trained to recognize the target person which is applied to            the one or more facial images.        -   Initiating another iteration in case the probability score            does not exceed a certain threshold.    -   Code instructions to output the face classification for use by        one or more face recognition based system.

In a further implementation form of the first and/or second aspects,each of the one or more positioning properties is a member of a groupconsisting of: a head pose of the target person, a property of one ormore potentially blocking object with respect to the target personand/or one or more environmental parameter affecting image capturing ofthe target person.

In a further implementation form of the first and/or second aspects, theoptimal facial image capturing position is defined by one or moreposition parameters of the drone. Each of the one or more positionparameters is a member of a group consisting of: a location of the dronewith respect to the target person, a distance of the drone from thetarget person, an altitude of the drone with respect to the targetperson and a view angle of the one or more imaging sensor mounted on thedrone with respect to the head pose identified for the target person.

In an optional implementation form of the first and/or second aspects,one or more operational parameters of one or more of the imaging sensorsare adjusted based on the one or more of the positioning properties. Theone or more operational parameter comprising a resolution, a zoom, acolor, a field of view, an aperture, a shutter speed, a sensitivity(ISO), a white balance and/or an auto exposure.

In a further implementation form of the first and/or second aspects, oneor more of the machine learning models are based on a neural network.

In a further implementation form of the first and/or second aspects, theface classification is done locally at the drone.

In a further implementation form of the first and/or second aspects, atleast part of the face classification is done by one or more remotesystems connected to the drone via one or more networks to receive theone or more images captured by the one or more imaging sensors mountedon the drone.

In an optional implementation form of the first and/or second aspects, aplurality of iterations are initiated to capture a plurality of facialimages depicting the face of the target person from a plurality of viewangles to form an at least partial three dimensional (3D) representationof at least part of a head of the target person.

In a further implementation form of the first and/or second aspects, theface recognition based system is an automated delivery system using theface classification for authenticating an identity of the target personfor delivery of goods to the target person.

In an optional implementation form of the first and/or second aspects,one or more of the iterations for recognizing the target person areinitiated in correlation with the goods at a time of delivery.

In an optional implementation form of the first and/or second aspects, alocation of the target person where the one or more images is capturedfor recognizing the target person is different form the location of thetarget person during the time of delivery.

In an optional implementation form of the first and/or second aspects,the drone is instructed to initiate one or more additionalauthentication sequences. The additional authentication sequencescomprising manual signature of the target person, a biometricauthentication of the target person and/or a voice authentication of thetarget person.

Other systems, methods, features, and advantages of the presentdisclosure will be or become apparent to one with skill in the art uponexamination of the following drawings and detailed description. It isintended that all such additional systems, methods, features, andadvantages be included within this description, be within the scope ofthe present disclosure, and be protected by the accompanying claims.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasksautomatically. Moreover, according to actual instrumentation andequipment of embodiments of the method and/or system of the invention,several selected tasks could be implemented by hardware, by software orby firmware or by a combination thereof using an operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of methods and/or systems as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars are shown by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a flowchart of an exemplary process of enhancingclassification of a face of a target person depicted in images capturedby drone mounted imaging sensors by dynamically positioning the drone toimprove visibility of the face in the images, according to someembodiments of the present invention; and

FIG. 2 is a schematic illustration of an exemplary system for enhancingclassification of a face of a target person depicted in images capturedby drone mounted imaging sensors by dynamically positioning the drone toimprove visibility of the face in the images, according to someembodiments of the present invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to facerecognition based on image analysis, and, more specifically, but notexclusively, to enhancing face recognition based on image analysis bydynamically adapting a position of a drone to improve visibility of theface in images captured by drone mounted imaging sensors.

According to some embodiments of the present invention, there areprovided methods, systems and computer program products for enhancingclassification of a face of a target person depicted in images capturedby drone mounted imaging sensors by dynamically and adaptivelypositioning the drone to improve visibility of the face in the images.The drone mounted with one or more imaging sensors, for example, acamera, a video camera, a night vision camera, an Infrared camera, athermal camera and/or the like may be therefore dynamically maneuveredto one or more positions with respect to the target person which areestimated to be optimal for capturing better visibility, higher qualityand/or increased accuracy images of the target person's face.

Moreover, the drone may be operated in an iterative process in which thedrone may be repositioned in a plurality of iterations and additionalimages of the target person's face may be captured from a plurality ofoptimal image capturing positions until the target person may beclassified and recognized with a sufficient degree of confidence.

During each iteration, one or more images of the target person capturedby the imaging sensor(s) mounted on the drone may be analyzed toidentify one or more positioning properties which may affect thevisibility, quality and/or accuracy of facial images captured by theimaging sensor(s) to depict the face of the target person, for example,a line of sight between the imaging sensor(s) and the face of the targetperson, visibility of the face of the target person and/or the like. Thepositioning property(s) may therefore have major impact on the abilityto accurately classify and recognize the face of the target person basedon analysis of the captured image(s).

The positioning properties may relate to the target person, specificallyto a head pose of the target person, to one or more objects in theenvironment of the target person which may potentially block the line ofsight between the imaging sensor(s) and the face of the target person,to one or more environmental parameters (e.g. illumination parameters,precipitation, etc.) and/or the like.

Based on analysis of the positioning properties, the drone may berepositioned at an estimated optimal image capturing position to improvevisibility, quality and/or resolution of the facial images of the targetperson captured by the imaging sensor(s).

The captured facial images may be processed by one or more classifiersas known in the art, for example, a Machine Learning (ML) model such as,for example, a neural network, a Support Vector Machine (SVM) and/or thelike trained to classify and recognize human faces, in particular theface of the target person. The ML model(s) may further compute aprobability score indicating a probability of correct classification ofthe target person's face which may reflect a confidence level ofcorrectly recognizing the target person and correctly determining hisidentity.

During each iteration the probability score may be compared to a certainthreshold defining the classification confidence level required forcorrectly recognizing the target person. In case the probability scorefails to exceed the threshold another iteration may be initiated toreposition the drone in another optimal image capturing position andcapture additional facial images of the target person's face which maybe further processed by the ML model(s) until the confidence levelthreshold is met.

Optionally, the probability score computed in a plurality of iterationsmay be aggregated to produce an aggregated probability score which maybe compared against the threshold.

Optionally, the drone may be operated in a plurality of iterations tocapture the face of the target person from a plurality of view angles inorder to form a Three Dimensional (3D) representation of at least partof the face and/or head of the target person.

Optionally, the image processing based recognition of the target personis supplemented by one or more additional authentication methods, forexample, a manual signature of the target person, a biometricauthentication of the target person, a voice authentication of thetarget person and/or the like.

The classification of the target person may be then provided to one ormore face recognition based systems, for example, an automated deliveryservice, a security system, a surveillance system and/or the like whichmay use the classification (identity) of the target person forauthentication and/or recognizing of one or more target personsmonitored and captured by the drone mounted imaging sensor(s).

Authenticating the target person based on images captured from optimalimage capturing positions selected based on analysis of the positioningproperties may present major advantages compared to existing methods andsystem for face recognition and people authentication.

First, most if not all of the face recognition based systems, servicesand platforms rely on automated face recognition which must be reliableand robust and compromising the face recognition reliability andconfidence may significantly undermine such automated systems andservices and may potentially render them useless. It is thereforeessential to ensure a high confidence level and reliable classificationand recognition for the automated process applied to classify andrecognize people faces.

Since the target person may move his head, his face may be positioned ina direction which may prevent the imaging sensor(s) from capturing highvisibility and/or high quality facial images of the target person.Therefore, analyzing the images to identify the head pose of the targetperson and position the drone in a position which enables a direct lineof sight between the imaging sensor(s) and the face of the target personmay enable capturing increased visibility and/or quality facial imagesof the target person which may in turn enable the ML model(s) toclassify the target person with higher confidence level.

Moreover, the environment in which the target person needs to berecognized may also present major limitations for capturing qualityfacial images which may allow the ML model(s) to correctly classifytarget persons with high confidence level. Various environments such asurban areas for example, may be populated with many objects which maypotentially block the line of sight between the drone mounted imagingsensor(s) and the face of the target person. Analyzing the imagesdepicting the environment of the target person to identify thepositioning properties relating to the potentially blocking object(s)may therefore enable to position the drone in a position which avoidsthe object(s) and enables a clear line of sight between the imagingsensor(s) and the face of the target person.

Furthermore, the environmental parameters (e.g. illumination, rain,snow, etc.) may have further impact on the quality of the facial imagesof the target person and may significantly degrade them. Therefore,analyzing the images to identify the environmental parameters andoperate the drone to a position which overcomes the limitations imposedby the environmental parameters may enable capturing increasedvisibility and/or quality facial images of the target person which mayenable the ML model(s) to classify the target person with higherconfidence level.

In addition, forming the 3D representation of the face and/or head ofthe target person using facial images captured from a plurality of viewangles may significantly improve immunity to fraud attacks initiated inattempt to impersonate as the genuine target person using a falserepresentation of the target person, for example, through a printedimage and/or a picture portrayed on a digital display. Since suchrepresentations are typically Two Dimensional (2D), creating andanalyzing the 3D representation of the face and/or head of the targetperson may easily reveal whether the identified face is a fraudulentrepresentation of the target person's face or the genuine face of thetarget person.

Therefore, adjusting the drone positioning to optimal image capturingpositions enabling its mounted imaging sensor(s) to capture highvisibility facial images of the target person may support using lesscomplex ML model(s), for example, neural networks which are simpler(less deep) compared to the ML models used by the existing facerecognition methods and system while maintaining high face recognitionaccuracy, reliability and robustness. This may significantly reduce thecomputing resources required for executing the reduced complexity MLmodel(s), for example, processing resources, storage resources,processing time and/or the like thus reducing time, costs and/or effortfor deploying the ML model(s). Moreover, the reduced complexity MLmodel(s) may be trained using significantly reduced computing resourcesthus further reducing time, costs and/or effort for deploying the MLmodel(s).

Moreover, by capturing high visibility facial images of the targetperson the imaging sensors mounted on the drone may be lower end andmore cost effective sensors having significantly lower quality and/orresolution compared to high end imaging sensors required by the facerecognition methods.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable storage medium can be a tangible devicethat can retain and store instructions for use by an instructionexecution device. The computer readable storage medium may be, forexample, but is not limited to, an electronic storage device, a magneticstorage device, an optical storage device, an electromagnetic storagedevice, a semiconductor storage device, or any suitable combination ofthe foregoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer program code comprising computer readable program instructionsembodied on a computer readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wire line,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

The computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

The computer readable program instructions for carrying out operationsof the present invention may be written in any combination of one ormore programming languages, such as, for example, assemblerinstructions, instruction-set-architecture (ISA) instructions, machineinstructions, machine dependent instructions, microcode, firmwareinstructions, state-setting data, or either source code or object codewritten in any combination of one or more programming languages,including an object oriented programming language such as Smalltalk, C++or the like, and conventional procedural programming languages, such asthe “C” programming language or similar programming languages.

The computer readable program instructions may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute the computerreadable program instructions by utilizing state information of thecomputer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Referring now to the drawings, FIG. 1 is a flowchart of an exemplaryprocess of enhancing classification of a face of a target persondepicted in images captured by drone mounted imaging sensors bydynamically positioning the drone to improve visibility of the face inthe images, according to some embodiments of the present invention.

An exemplary process 100 may be executed to adaptively and dynamicallymaneuver a drone mounted with one or more imaging sensors to positionthe drone with respect to a target person in a position optimized forcapturing improved and better visibility images of the target person'sface. Moreover, the process 100 may be an iterative process in which thedrone may be repositioned in a plurality of iterations and additionalimages of the target person's face may be captured until the targetperson may be classified and recognized with sufficient degree ofconfidence.

Reference is also made to FIG. 2, which is a schematic illustration ofan exemplary system for enhancing classification of a face of a targetperson depicted in images captured by drone mounted imaging sensors bydynamically positioning the drone to improve visibility of the face inthe images, according to some embodiments of the present invention. Anexemplary system may be deployed for adaptively and dynamicallymaneuvering a drone 202 mounted with one or more imaging sensors 216 toposition the drone 202 with respect to a target person 204 in a positionoptimized for capturing improved and better visibility images of theface of the target person 204 and thus enable accurate recognition ofthe target person 204.

The drone 202 mounted with the imaging sensor(s) 216, for example, acamera, a video camera, a night vision camera, an Infrared camera, athermal camera and/or the like may further comprise a network interface210 for connecting to a network 208, a processor(s) 212 for executingthe process 100 and/or part thereof, and a storage 214 for storing dataand/or program (program store).

The network interface 210 may include one or more wireless networkand/or communication interfaces, for example, a Wireless Local AreaNetwork (WLAN) interface, a cellular interface and/or the like to enablethe drone 202 to communicate with one or more remote network resourcesvia the network 208. The network 208 may comprise one or more wiredand/or wireless networks, for example, a Local Area Network (LAN), aWLAN, a Wide Area Network (WAN), a Municipal Area Network (MAN), acellular network, the internet and/or the like.

The processor(s) 212, homogenous or heterogeneous, may include one ormore processors arranged for parallel processing, as clusters and/or asone or more multi core processor(s). The storage 214 may include one ormore non-transitory persistent storage devices, for example, a Read OnlyMemory (ROM), a Flash array, a hard drive and/or the like. The storage214 may also include one or more volatile devices, for example, a RandomAccess Memory (RAM) component, a cache memory and/or the like.

The processor(s) 212 may execute one or more software modules such as,for example, a process, a script, an application, an agent, a utility, atool and/or the like each comprising a plurality of program instructionsstored in a non-transitory medium (program store) such as the storage214 and executed by one or more processors such as the processor(s) 212.For example, the processor(s) 212 may execute a local face recognitionapplication (app) 220A for executing the process 100 and/or partthereof. The local face recognition app 220A may optionally utilizeand/or facilitate one or more hardware elements integrated and/orutilized in the drone 202, for example, a circuit, a component, anIntegrated Circuit (IC), an Application Specific Integrated Circuit(ASIC), a Field Programmable Gate Array (FPGA), a Digital SignalsProcessor (DSP) and/or the like.

Optionally, the process 100 and/or part thereof is executed by one ormore remote face recognition systems 206, for example, a computer, aserver, a computing node, a cluster of computing nodes and/or the likeconnected to the network 208 and communicating with the drone 202. Theremote face recognition system 206 may comprise a network interface 230for connecting to the network 208, a processor(s) 232 such as theprocessor(s) 212 for executing the process 100 and/or part thereof, anda storage 234 such as the storage 214 for storing data and/or program(program store).

The network interface 230 may include one or more wired and/or wirelessnetwork interfaces, for example, a LAN interface, a WLAN interface, aWAN interface, a MAN interface, a cellular network interface and/or thelike for connecting to the network 208 and enabling the remote facerecognition system 206 to communicate with one or more remote networkresources, in particular with the drone 202. In addition to thepersistent and/or volatile memory resources, the storage 234 may furtherinclude one or more network storage resources, for example, a storageserver, a network accessible storage (NAS), a network drive, a cloudstorage and/or the like accessible via the network interface 230.

The processor(s) 232 may execute one or more software modules eachcomprising a plurality of program instructions stored in anon-transitory medium (program store) such as the storage 234 andexecuted by one or more processors such as the processor(s) 232. Forexample, the processor(s) 232 may execute a remote face recognition app220B for executing the process 100 and/or part thereof. The remote facerecognition app 220B may optionally utilize and/or facilitate one ormore hardware elements integrated and/or utilized in the remote facerecognition systems 206, for example, a circuit, a component, an IC, anASIC, an FPGA, a DSP and/or the like.

Optionally, the remote face recognition system 206 and/or the remoteface recognition app 220B executed by the face recognition system 206are implemented as one or more cloud computing services, for example, anInfrastructure as a Service (IaaS), a Platform as a Service (PaaS), aSoftware as a Service (SaaS) and/or the like such as, for example,Amazon Web Service (AWS), Google Cloud, Microsoft Azure and/or the like.

Moreover, in some embodiments of the present invention, the process 100may be executed jointly by the local face recognition app 220A and theremote face recognition app 220B such that one or more steps of theprocess 100 are executed by the local face recognition app 220A and oneor more other steps of the process 100 are executed by the remote facerecognition app 220B. For example, the local face recognition app 220Amay control operation of the drone 202 and/or the imaging sensor(s) 216and transmit the captured images to the remote face recognition app 220Bwhich may apply the ML model(s) to analyze the images and recognize thetarget person 204. Therefore, for brevity, a single and/or unified facerecognition app 220 is designated herein for executing the process 100which may comprise the local face recognition app 220A, the remote facerecognition app 220B and/or any combination thereof.

The face recognition app 220 may provide the classification of thetarget person 204, in particular, the recognition results of the targetperson 204, for example, the identity of the target person 204 to one ormore face recognition based systems 240, for example, an automateddelivery service which is deployed to deliver goods, mail and/or thelike to the target person via the drone 202. The face recognition basedsystem(s) 240 may be connected to the network 208 such that the facerecognition app 220 may communicate with the face recognition basedsystem(s) 240 via the network 208 to provide, i.e., transmit theclassification (identity) of the target person 204 to the facerecognition based system(s) 240.

Optionally, one or more of the face recognition based systems 240integrate the remote face recognition system 206.

The process 100 and the system presented in FIG. 2 describe a singledrone 202 and a single target person 204. However, this should not beconstrued as limiting as the process may be extended for a plurality ofdrones such as the drone 202 and/or a plurality of target persons suchas the target person 204.

As shown at 102, the process 100 starts with the face recognition app220 receiving one or more images captured by one or more of the imagingsensors 216 mounted on the drone 202. In particular, the image(s) arecaptured to depict the target person 204 while the drone 202 isoperated, maneuvered and/or positioned to approach the target person204.

As shown at 104, the face recognition app 220 may analyze the capturedimage(s) depicting the target person 204 to identify one or morepositioning properties of the target person 204 which may affect imagecapturing of the target person 204 by the imaging sensor(s) 216 mountedon the drone 202. The face recognition app 220 may employ one or moremethods, techniques and/or algorithms as known in the art for analyzingthe captured image(s) to identify and extract the positioningproperty(s), for example, image processing, computer vision, ML basedclassification and/or the like.

In particular, the face recognition app 220 may analyze the capturedimage(s) to identify positioning property(s) which may affect imagecapturing of the face of the target person 204, for example, a line ofsight of the imaging sensor(s) 216 to the face of the target person 204,visibility of the face of the target person 204 and/or the like. Thepositioning property(s) may therefore have major impact on the abilityto accurately classify and recognize the face of the target person 204based on analysis of the captured image(s).

For example, one or more of the positioning properties may relate to thetarget person 204 himself, for example, one or more of the positioningproperties may express a head pose of the target person 204. The facerecognition app 220 may analyze the captured image(s) to identify thehead pose of the target person 204 which may be expressed in one or morespatial representations, for example, yaw, pitch and roll. For example,the face recognition app 220 may identify the head pose of the targetperson 204 expressed in six dimensions as known in the art by threetranslations, three rotations and a rotation order. In another example,the face recognition app 220 may identify the head pose of the targetperson 204 expressed in six dimensions by three translations and fourquaternions.

In another example, one or more of the positioning properties may relateto one or more objects in the environment of the target person 204,specifically blocking objects which may potentially block the line ofsight between the imaging sensor(s) 216 mounted on the drone 202 and theface of the target person 204. As such one or more of the positioningproperties may express one or more properties such as, for example, alocation, shape, width, height and/or the like of one or more objects,for example, a structure, a vehicle, a pole, a tree and/or the likewhich may potentially block and/or obscure at least partially the lineof sight between the drone mounted imaging sensor(s) 216 and the targetperson 204, specifically the line of sight to the face of the targetperson 204. In another example, the face recognition app 220 may analyzethe captured image(s) to identify that the target person 204 is wearinga hat, a hood, and/or the like which may block at least partially theline of sight between the imaging sensor(s) 216 and the face of thetarget person 204.

In another example, one or more of the positioning properties may relateto one or more environmental parameters, for example, illuminationparameters, rain and/or the like. As such one or more of the positioningproperties may express an illumination level of the target person 204,specifically illumination level of the face of the target person 204which may affect visibility and/or quality of the face image depicted inthe captured images(s). In another example, one or more of thepositioning properties may express existence of precipitation, forexample, rain, snow and/or the like which may affect visibility and/orquality of the face image depicted in the captured images(s).

As shown at 106, the face recognition app 220 may instruct the drone 202to adjust its position with respect to the target person 204 to aposition which is estimated by the face recognition app 220, based onanalysis of the identified positioning property(s), to be an optimalfacial capturing position from which the imaging sensor(s) 204 maycapture higher visibility, increased quality and/or improved accuracyimages of the face of the target person 204.

The optimal facial capturing position may be defined by one or morespatial position parameters of the drone 202, for example, a location ofthe drone 202 with respect to the target person 204, a distance of thedrone 202 from the target person 204, an altitude of the drone 202 withrespect to the target person 204, a view angle (of the line of sight) ofthe imaging sensor(s) 216 mounted on the drone 202 with respect to theface or head pose identified for the target person 204 and/or the like.

The face recognition app 220 may instruct the drone 202 to reposition,maneuver and/or approach the optimal facial capturing position bycommunicating with one or more operation and/or navigation systemscontrolling the operation and movement of the drone 202. For example,the face recognition app 220 may transmit the coordinates of the optimalfacial capturing position to the navigation system of the drone 202 andinstruct the navigation system to reposition the drone in thetransmitted coordinates.

For example, assuming that based on the head pose of the target person204 identified by analyzing the captured image(s), the face recognitionapp 220 determines that the face of the target person 204 is facing acertain direction which is not aligned with a line of sight between theimaging sensor(s) 216 and the face of the target person 204. In suchcase, the face recognition app 220 may instruct the drone 202 toreposition along a line extending perpendicularly from the face of thetarget person 204 to the certain direction to enable the imagingsensor(s) 216 to capture higher visibility, increased quality and/orimproved accuracy images of the face of the target person 204.

In another example, assuming that based on the identified positioningproperty(s), the face recognition app 220 determines that the targetperson 204 is wearing a baseball cap which blocks at least partially theline of sight between the imaging sensor(s) 216 and the face of thetarget person 204 while the drone 202 is located in its current positionwhich may be at least slightly elevated with respect to the face of thetarget person 204. In such case, the face recognition app 220 mayinstruct the drone 202 to reposition at a lower altitude position withrespect to the target person 204 such that the baseball cap does notinterfere with the line of sight between the drone mounted imagingsensor(s) 216 and the face of the target person 204 to enable theimaging sensor(s) 216 to capture higher visibility, increased qualityand/or improved accuracy images of the face of the target person 204.

In another example, assuming that based on the identified positioningproperty(s), the face recognition app 220 determines that a certainstructure, for example, a building balcony is blocking at leastpartially the line of sight between the imaging sensor(s) 216 and theface of the target person 204 while the drone 202 is located in itscurrent position. In such case, the face recognition app 220 mayinstruct the drone 202 to reposition at a different position, location,altitude which is estimated by the face recognition app 220 to have aclear line of sight to the face of the target person 204, specificallywith respect to the building balcony to enable the imaging sensor(s) 216to capture higher visibility, increased quality and/or improved accuracyimages of the face of the target person 204.

In another example, assuming that based on the identified positioningproperty(s), the face recognition app 220 determines that it iscurrently raining and the rain obscures at least partially the face ofthe target person 204. In such case, the face recognition app 220 mayinstruct the drone 202 to reposition at a closer position to the targetperson 204 which is estimated by the face recognition app 220 to besufficiently close to enable the imaging sensor(s) 216 to capture highervisibility, increased quality and/or improved accuracy images of theface of the target person 204. In another example, assuming that basedon the identified positioning property(s), the face recognition app 220determines that the face of the target person is illuminated only from acertain direction. In such case, the face recognition app 220 mayinstruct the drone 202 to reposition at a different position in thedirection of the illumination source with respect to the target person204 which is therefore estimated by the face recognition app 220 toenable the imaging sensor(s) 216 to higher visibility, increased qualityand/or improved accuracy images of the face of the target person 204under improved illumination conditions.

Optionally, based on analysis of the identified positioning property(s),the face recognition app 220 may instruct the drone 202 to adjust one ormore operational parameters of the imaging sensor(s) 216, for example, aresolution, a zoom, a color, a field of view, an aperture, a shutterspeed, a sensitivity (ISO), a white balance, an auto exposure and/or thelike. For example, assuming that the optimal image capturing positionselected by the face recognition app 220 based on the identifiedpositioning property(s) is relatively far from the target person 204. Insuch case the face recognition app 220 may instruct increasing the zoomof the imaging sensor(s) 216 to capture a higher quality and/orincreased visibility of the face of the target person 204. In anotherexample, assuming that according to the identified positioningproperty(s) the face of the target person 204 is illuminated with littleillumination. In such case the face recognition app 220 may instructincreasing the shutter speed of the imaging sensor(s) 216 to enable morelight to penetrate the sensor and thus capture a higher quality and/orincreased visibility of the face of the target person 204.

As shown at 108, the face recognition app 220 may receive one or morefacial images of the target person 204 captured by the imaging sensor(s)216 after the drone 202 has positioned itself at the optimal imagecapturing position identified and/or estimated by the face recognitionapp 220.

As shown at 110, the face recognition app 220 may provide, transmitand/or output one or more of the facial images of the target person 204to one or more classifiers as known in the art, for example, an ML modelsuch as, for example, a neural network, an SVM and/or the like trainedto classify and recognize human faces, specifically the face of thetarget person 204. The ML model(s) may be utilized by one or moretrained neural networks, for example, a Convolutional Neural Network(CNN) as known in the art which is highly efficient for image analysisapplied for object detection and/or face classification and recognition.

As known in the art, the ML model(s) are configured and trained toextract feature vectors from the images and classify the extractedfeature vectors to respective classes (labels), for example, anidentifier (identity) of the target person according to matching of thefeature vectors with feature vectors extracted from training images ofthe target person 204 processed and learned by the ML model(s) duringtheir training phase.

The ML model(s) may typically compute a probability score (confidencescore) indicating a probability (confidence level) of correctclassification of the face of the target person 204 and hence theprobability and confidence of correctly recognizing the target person204.

The probability score expressing confidence of the correct recognitionmay be interpreted as the distance between the feature vector(s)extracted from the facial image(s) of the target person 204 captured bythe imaging sensor(s) 216 from the optimal image capturing position andfeature vectors which are extracted from training facial images of thetarget person 204 which were used to train the ML model(s). For example,as known in the art, cosine similarity may be measured between featurevectors A·B extracted from facial image(s) of the target person 204captured by the imaging sensor(s) 216 and ∥A∥ ∥B˜ extracted from thetraining facial image(s) of the target person 204 as follows: A·B=∥A∥∥B∥ cos Θ. In such implementation, the higher the value of Θ the loweris the confidence of correct classification and recognition, i.e., thelower is the probability score and vice versa, the lower the value of Θthe higher is the confidence of correct classification and recognition,i.e., the higher is the probability score.

As shown at 112, the face recognition app 220 may receive from the MLmodel(s) the classification, i.e., the label of the face of targetperson 204 which may include the identity of the target person 204associated with the probability score computed by the ML model(s) toexpress the confidence of correctly classifying and recognizing thetarget person 204.

As shown at 114, which is a conditional step, the face recognition app220 may compare the probability score computed by the ML model(s) forthe classification of the face of the target person 204 to a certainthreshold defined to ensure sufficient confidence that the target person204 is correctly recognized.

In case the probability score exceeds the threshold, the facerecognition app 220 may determine that the target person 204 isrecognized with high confidence and the process 100 may branch to 116.However, in case the probability score does not exceed the threshold,the face recognition app 220 may determine that the target person 204 isnot recognized with sufficient confidence and the process 100 may branchto 102 to initiate another iteration for repositioning the drone 202 inanother optimal image capturing position which is estimated by the facerecognition app 220 to enable the imaging sensor(s) 204 to capture theface of the target person 204 with increased visibility, quality and/oraccuracy.

The threshold used by the face recognition app 220 to evaluate and/ordetermine successful recognition of the target person may be predefined.However, according to some embodiments of the present invention, thethreshold may not be static and absolute but rather adjustabledynamically according to one or more parameters, for example, quality ofthe captured facial images and/or the like.

Optionally, the face recognition app 220 and/or the ML model(s) mayaggregate the probability score computed in a plurality of iterations ofthe process 100, i.e., based on facial images captured by the imagingsensor(s) 216 from a plurality of optimal image capturing positionsestimated by the face recognition app 220 in the plurality ofiterations. The face recognition app 220 may therefore compare theaggregated probability score to the threshold in order to determinewhether the target person 204 is recognized with a sufficiently highconfidence level.

Optionally, the face recognition app 220 may initiate a plurality ofiterations to instruct the drone to reposition in a plurality ofpositions with respect to the target person 204 in order to capture aplurality of facial images depicting the face of the target person 204from a plurality of view angles to form an at least partial 3Drepresentation of at least part of the head and/or the face of thetarget person 204. This may enable the face recognition app 220 and/orthe ML model(s) to detect and possibly prevent potential frauds in whicha malicious party may attempt to fraudulently impersonate as the targetperson 204 by presenting the face of the target user 204 via one or moreTwo Dimensional (2D) means, for example, a picture of the target user204 printed on a page, portrayed on a screen of a device (e.g. a tablet,etc.) and/or the like. Creating the at least partial 3D model of thehead and/or face of the target person 204 may enable the facerecognition app 220 and/or the ML model(s) to identify that the face isa 2D dimensional presentation and may be thus invalid. Moreover, ascenario in which the drone 202 is moving but a head pose of the targetperson 204 as identified by the face recognition app 220 identifies doesnot change with respect to the moving drone 202 may be highly indicativeof a potential fraud.

As shown at 116, after determining that the target person 204 isrecognized with a sufficient confidence1 level, the face recognition app220 may output the classification of the target person 204, for example,the identity of the target person 204 to one or more of the facerecognition based systems 240.

According to some embodiments of the present invention, the facerecognition based system 240 is an automated delivery service which usesdrones such as the drone 202 to deliver goods, for example, products,mail and/or the like to target persons 240 registered to the service.

The automated delivery service 240 may therefore use the classificationof the target user 204, i.e., the identity of the target user toauthenticate the identity of the target person 204 to whom the goodsshould be delivered. This means that assuming the drone 202 carriesgoods for delivery to the target person 204, the automated deliveryservice 240 may first authenticate the target person 204 based on theclassification received from the face recognition app 220 to verify thatthe target person 204 detected by the drone 202 is indeed the sameperson to whom the goods should be delivered.

After authenticating the identity of the target person 204, the dronemay be operated and/or instructed to actually deliver the goods to thetarget person 204.

Optionally, the face recognition app 220 may, optionally in response toa request from the automated delivery service 240, initiate one or moreiterations of the process 100 to capture one or more images of thetarget person 204 in correlation with the goods at the time of deliveryand/or after delivered to the target person 204. For example, the facerecognition app 220 may initiate one or more iterations of the process100 to capture image(s) of the target person 204 receiving the goodsfrom the drone 202, holding the goods, the goods located next to thetarget person 204 and/or the like. Such images may be logged andoptionally used as evidence of delivery of the goods to the targetperson 204.

Optionally, the delivery point (location) where the drone 202 deliversthe goods to the target person 204 (after authenticated) is differentfrom the optimal image capturing position selected during one or moreiterations of the process 100. After recognizing the target person 204with sufficient confidence level, i.e., the probability score exceedsthe threshold, the face recognition app 220 may instruct the drone 202to maneuver to another location—a delivery point, where the drone 202may deliver the goods to the target person 204. This may enable the facerecognition app 220 to select the optimal image capturing position(s) toenable the ML model(s) to recognize the target person 204 withsufficient confidence before moving to the delivery point.

Optionally, the face recognition app 220 employs one or more additionalauthentication sequences, methods and/or means for authenticating thetarget person 204 in order to verify his identity and ensure that he isthe genuine target person to whom the goods should be delivered. Theadditional authentication sequences, methods and/or means may include,for example, a manual signature of the target person 204, a biometricauthentication of the target person 204, a voice authentication of thetarget person 204 and/or the like. For example, after authenticating thetarget person 204 with sufficient confidence level, the face recognitionapp 220 may instruct the drone 202 to maneuver to a certain position inclose proximity to the target person 204 and initiate one or morebiometric authentications. For example, the drone 202 may be operated toinstruct the target person 204 to sign his name on a digital surface ora touchscreen integrated in the drone 202. The signature may be thenanalyzed in comparison to a reference signature of the target person 204stored and available from the automated delivery system 240. In anotherexample, the drone 202 may be operated to instruct the target person 204to place his finger on a finger print reader integrated in the drone202. The finger print of the target person 204 may be then analyzed incomparison to a reference finger print of the target person 204 storedand available from the automated delivery system 240. In anotherexample, the drone 202 may be operated to instruct the target person 204to say a few words and/or a predefined sequence of words which may becaptured and recorded by one or more audio input device (e.g.microphone) integrated in the drone 202. The recorded speech of thetarget person 204 may be then analyzed in comparison to a referencespeech of the target person 204 stored and available from the automateddelivery system 240.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

It is expected that during the life of a patent maturing from thisapplication many relevant systems, methods and computer programs will bedeveloped and the scope of the terms ML models and neural networks areintended to include all such new technologies a priori.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”. This termencompasses the terms “consisting of” and “consisting essentially of”.

The phrase “consisting essentially of” means that the composition ormethod may include additional ingredients and/or steps, but only if theadditional ingredients and/or steps do not materially alter the basicand novel characteristics of the claimed composition or method.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

The word “exemplary” is used herein to mean “serving as an example, aninstance or an illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals there between.

The word “exemplary” is used herein to mean “serving as an example, aninstance or an illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable sub-combination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting. In addition, any priority document(s) of this applicationis/are hereby incorporated herein by reference in its/their entirety.

What is claimed is:
 1. A computer implemented method of increasingreliability of face recognition in analysis of images captured by dronemounted imaging sensors, comprising: recognizing a target person in atleast one iteration comprising: identifying at least one positioningproperty of the target person based on analysis of at least one imagecaptured by at least one imaging sensor mounted on a drone operated toapproach the target person, instructing the drone to adjust its positionto an optimal facial image capturing position selected based on the atleast one positioning property, receiving at least one facial image ofthe target person captured by the at least one imaging sensor while thedrone is located at the optimal facial image capturing position,receiving a face classification associated with a probability score fromat least one machine learning model trained to recognize the targetperson which is applied to the at least one facial image, and initiatinganother iteration in case the probability score does not exceed acertain threshold; and outputting the face classification for use by atleast one face recognition based system.
 2. The computer implementedmethod of claim 1, wherein the at least one positioning property is amember of a group consisting of: a head pose of the target person, aproperty of at least one potentially blocking object with respect to thetarget person and at least one environmental parameter affecting imagecapturing of the target person.
 3. The computer implemented method ofclaim 2, wherein the optimal facial image capturing position is definedby at least one position parameter of the drone, the at least oneposition parameter is a member of a group consisting of: a location ofthe drone with respect to the target person, a distance of the dronefrom the target person, an altitude of the drone with respect to thetarget person and a view angle of the at least one imaging sensormounted on the drone with respect to the head pose identified for thetarget person.
 4. The computer implemented method of claim 1, furthercomprising adjusting at least one operational parameter of the at leastone imaging sensor based on the at least one positioning property, theat least one operational parameter is a member of a group consisting of:a resolution, a zoom, a color, a field of view, an aperture, a shutterspeed, a sensitivity (ISO), a white balance and an auto exposure.
 5. Thecomputer implemented method of claim 1, wherein the at least one machinelearning model is based on a neural network.
 6. The computer implementedmethod of claim 1, wherein the face classification is done locally atthe drone.
 7. The computer implemented method of claim 1, wherein atleast part of the face classification is done by at least one remotesystem connected to the drone via at least one network to receive the atleast one image captured by the at least one imaging sensor mounted onthe drone.
 8. The computer implemented method of claim 1, furthercomprising initiating a plurality of iterations to capture a pluralityof facial images depicting the face of the target person from aplurality of view angles to form an at least partial three dimensional(3D) representation of at least part of a head of the target person. 9.The computer implemented method of claim 1, wherein the face recognitionbased system is an automated delivery system using the faceclassification for authenticating an identity of the target person fordelivery of goods to the target person.
 10. The computer implementedmethod of claim 9, further comprising initiating the at least oneiteration for recognizing the target person in correlation with thegoods at a time of delivery.
 11. The computer implemented method ofclaim 10, further comprising a location of the target person where theat least one image is captured for recognizing the target person isdifferent form the location of the target person during the time ofdelivery.
 12. The computer implemented method of claim 9, furthercomprising instructing the drone to initiate at least one additionalauthentication sequence which is a member of a group consisting of:manual signature of the target person, a biometric authentication of thetarget person and a voice authentication of the target person.
 13. Asystem for increasing reliability of face recognition in analysis ofimages captured by drone mounted imaging sensors, comprising: at leastone processor executing a code, the code comprising: code instructionsto recognize a target person in at least one iteration comprising:identifying at least one positioning property of the target person basedon analysis of at least one image captured by at least one imagingsensor mounted on a drone located in operated to approach the targetperson, instructing the drone to adjust its position to an optimalfacial image capturing position selected based on the at least onepositioning property, receiving at least one facial image of the targetperson captured by the at least one imaging sensor while the drone islocated at the optimal facial image capturing position, receiving a faceclassification associated with a probability score from at least onemachine learning model trained to recognize the target person which isapplied to the at least one facial image, and initiating anotheriteration in case the probability score does not exceed a certainthreshold; and code instructions to output the face classification foruse by at least one face recognition based system.